Financial institutions and businesses involved with sales of property have long tried to estimate values of property accurately. Accurate estimation serves many important purposes. For example, financial institutions use property value estimates as one of the key factors in calculating the loan to value (LTV) ratio of a home. The LTV ratio is the ratio of a first mortgage (or the total of all mortgage liens (TLTV)) to the appraised or estimated value of the real property. The LTV ratio is an important calculation used by financial institutions to assess lending risks. For example, as the LTV ratio of a property increases, the likelihood of loan default increases. In addition, when a default does occur, the higher the LTV ratio, the greater the potential financial loss to the financial institution. Moreover, financial institutions may “mark-to-market” their portfolio of outstanding loans to determine the current LTV ratios of the mortgages. Mark-to-market is an accounting methodology used to calculate current LTV ratio of outstanding loans. Accordingly, the accuracy of the estimated value of real estate used to calculate the LTV ratio is critical.
One technique for estimating the value of real estate utilizes a repeat sales index. A repeat sales index may be used to identify housing market conditions and the amount of equity homeowners have gained through house price appreciation. The index itself is a composite of changes in individual home prices within a geographical region, such as a municipality, zip code, county, region, or state. The data used in the repeat sales index may comprise successive selling prices and the sale dates for the same property (e.g., a residential home). By using pricing of the same property, the repeat sales index eliminates the inherent bias in price changes that are not due to the true house price change, but due to external factors such as, for example, consumer trends for bigger houses.
The basic repeat sales index may be improved through the use of data from refinance transactions, in addition to data from purchase transactions, in forming repeat sales indices, thereby increasing the size of the estimation sample and the timeliness of the evaluation sample. Moreover, as disclosed in U.S. Pat. No. 6,401,070, the data used in a repeat sales index may be weighted to provide particular importance to one set of data over another. The content of U.S. Pat. No. 6,401,070 is incorporated herein by reference in its entirety.
There are qualitative differences between house price data derived from purchase transactions and from refinance transactions. Purchase transactions typically involve arms-length agreements in which the incentives of the parties will tend to result in an unbiased sales price, and the information of the three parties (buyer, seller, and appraiser) will tend to result in greater accuracy in ascertaining the value of the property, Refinance transactions, on the other hand, have valuation based solely on an appraisal and consequently are subject to several sources of bias. For example, incentive biases in appraisals arise because appraisers are motivated to arrive at valuations that can make the refinance transaction successful. Selection biases arise because, particularly in a down market, the properties that are eligible for refinance are more likely to be those that have appreciated relative to the market as a whole.
A repeated sales index that factors in such biases to the data is generically referred to as a weighted repeat sales index (WRSI). WRSI also refers to indexes that include refinance transactions as well as property sale transactions, and indexes with and without weights on the transactions. As disclosed in U.S. Pat. No. 6,401,070, the WRSI may be expressed as:log(Ps/Pt)=Is−It+ds2Rs2−dt1Rt1÷ξ  (1)
Here, the variable Pt is the first transaction price, Ps is the second transaction price, It is the log house price index (HPI) value at time t, Rt1 is equal to one (1) if the first transaction is a refinance and equal to zero (0) otherwise, Rs2 is equal to one (1) if the second transaction is a refinance and equal to zero (0) otherwise, dt1 is a coefficient representing the first transaction refinance (REFI) bias at time t, ds2 is coefficient representing the second transaction refinance (REFI) bias at time s, and ξ is the error term. In essence, the refinance bias terms measure the difference in appreciation between purchase and refinance transactions at the two dates. Accordingly, the WRSI model of equation (1) allows for time varying differences between refinance and purchase transactions, thereby improving index accuracy.
As used herein, “aggregated level” refers to a geographic region comprised of more than one smaller geographic regions. For example, a state may be an aggregated level of counties and zip codes. As used herein, “disaggregated level” refers to a geographic region that may be included in an aggregated level. For example, a county and a zip code may be disaggregated levels of a state.
The HPI and REFI values that that are used in the WRSI model may be estimated using an ordinary least square (OLS) regression. However, HPI and REFI index estimation using OLS yields excessively volatile and inaccurate estimates, especially at disaggregated levels.
Accordingly, systems and methods are needed that provide a better estimation of the HPI and REFI values that are used in a home price index model. Systems and methods consistent with the present invention address the difficulties discussed above by providing a regularized, adjusted WRSI that calculates a more accurate estimated value of real estate growth rates at aggregated and disaggregated levels, among other things.